Industrial Robotic Perception
Few-/zero-shot anomaly detection and open-vocabulary grasp pose estimation for industrial robotic manipulation. Hyundai–NTU–A*STAR Corporate Lab · Nov 2025 – Present
Funding: Hyundai–NTU–A*STAR Corporate Lab · Nov 2025 – Present
Building the perception stack for reliable robotic part picking at HMGICS: open-world object detection and segmentation, 6D pose estimation, and grasp pose generation for cluttered industrial scenes, alongside foundation-model-based anomaly detection for unseen part categories.
Contributions:
- Developed DriftAD, a visually-guided CLIP adaptation framework for few-shot industrial anomaly detection, achieving up to 1.5-point gains in 1-shot AUROC and PRO (ACM MM 2026).
- Extended the work to zero-shot anomaly detection with TRACE, combining CLIP semantic evidence with DINOv3 structural features for cross-model anomaly detection without target-domain examples (submitted to AAAI 2027).
- Built an open-vocabulary perception and grasping pipeline predicting detections, instance masks, and grasp poses for object categories unseen during training, validated across 77 categories of real industrial parts with an on-site robotic grasping demo.
- Filed a patent for grasp pose estimation on wrapped parts in unstructured industrial environments.
Related publications: ACM MM 2026